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DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services (2401.10175v1)

Published 18 Jan 2024 in cs.HC

Abstract: A hybrid society is expected to emerge in the near future, with different mobilities interacting together, including cars, micro-mobilities, pedestrians, and robots. People may utilize multiple types of mobilities in their daily lives. As vehicle automation advances, driver modeling flourishes to provide personalized intelligent services. Thus, modeling drivers across mobilities would pave the road for future society mobility-as-a-service, and it is particularly interesting to predict driver behaviors in newer mobilities with traditional mobility data. In this work, we present takeover prediction on a micro-mobility, with car simulation data.The promising model performance demonstrates the feasibility of driver modeling across mobilities, as the first in the field.

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References (39)
  1. Qaisar Abbas and Abdullah Alsheddy. 2020. Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis. Sensors 21, 1 (2020), 56.
  2. The role of micro-mobility in shaping sustainable cities: A systematic literature review. Transportation research part D: transport and environment 92 (2021), 102734.
  3. A classification model for sensing human trust in machines using EEG and GSR. ACM Transactions on Interactive Intelligent Systems (TiiS) 8, 4 (2018), 1–20.
  4. Design a Sustainable Micro-mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques. arXiv preprint arXiv:2210.11714 (2022).
  5. Felix Becker and Kay W Axhausen. 2017. Literature review on surveys investigating the acceptance of automated vehicles. Transportation 44, 6 (2017), 1293–1306.
  6. Francois Chollet et al. 2015. Keras. https://github.com/fchollet/keras
  7. ADAPT: Awesome Domain Adaptation Python Toolbox. arXiv preprint arXiv:2107.03049 (2021).
  8. Nachiket Deo and Mohan M Trivedi. 2019. Looking at the driver/rider in autonomous vehicles to predict take-over readiness. IEEE Transactions on Intelligent Vehicles 5, 1 (2019), 41–52.
  9. US department of transportation. 2019. Bikeshare and E-scooter Systems in the US. https://data.bts.gov/stories/s/Bikeshare-and-e-scooters-in-the-U-S-/fwcs-jprj/
  10. Predicting driver takeover performance in conditionally automated driving. Accident Analysis & Prevention 148 (2020), 105748.
  11. Adoption of micro-mobility solutions for improving environmental sustainability: comparison among transportation systems in urban contexts. Sustainability 14, 13 (2022), 7960.
  12. Empatica. 2023. E4 Sensor. https://www.empatica.com/research/e4/
  13. The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk. Frontiers in psychology 14 (2023), 1129583.
  14. Modeling takeover behavior in level 3 automated driving via a structural equation model: Considering the mediating role of trust. Accident Analysis & Prevention 157 (2021), 106156.
  15. Driver distraction detection methods: A literature review and framework. IEEE Access 9 (2021), 60063–60076.
  16. Learn-able Evolution Convolutional Siamese Neural Network for Adaptive Driving Style Preference Prediction. In 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE, 1–7.
  17. Oliver Kramer and Oliver Kramer. 2016. Scikit-learn. Machine learning for evolution strategies (2016), 45–53.
  18. Andrew L Kun et al. 2018. Human-machine interaction for vehicles: Review and outlook. Foundations and Trends® in Human–Computer Interaction 11, 4 (2018), 201–293.
  19. Planning for e-scooter use in metropolitan cities: A case study for Paris. Transportation research part D: transport and environment 100 (2021), 103037.
  20. Shuo Li. 2019. Investigating older drivers’ takeover performance and requirements to facilitate safe and comfortable human-machine interactions in highly automated vehicles. Ph. D. Dissertation. Newcastle University.
  21. A review of psychophysiological measures to assess cognitive states in real-world driving. Frontiers in human neuroscience 13 (2019), 57.
  22. Health impacts of electric micromobility transitions in Barcelona: A scenario analysis. Environmental Impact Assessment Review 96 (2022), 106836.
  23. Cosmate Co. Ltd. 2020. MB200 6 degree of freedom motion base.
  24. Trust in Shared Automated Vehicles: Study on Two Mobility Platforms. arXiv preprint arXiv:2303.09711 (2023).
  25. Does prior experience influence trust in novel automation systems? A study on two mobility platforms. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications Sage CA: Los Angeles, CA, 21695067231192415.
  26. Human-Machine Interfaces and Vehicle Automation: A Review of the Literature. Accident Analysis & Prevention 109 (2022), 18–28.
  27. Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing 10, 1 (2017), 18–31.
  28. Micromobility and public transport integration: The current state of knowledge. Transportation Research Part D: Transport and Environment 89 (2020), 102628.
  29. Deeptake: Prediction of driver takeover behavior using multimodal data. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–14.
  30. Incorporating gaze behavior using joint embedding with scene context for driver takeover detection. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4633–4637.
  31. Brian Richards. 2012. Future transport in cities. Taylor & Francis.
  32. Kristina Schaaff and Marc TP Adam. 2013. Measuring emotional arousal for online applications: Evaluation of ultra-short term heart rate variability measures. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, 362–368.
  33. Shimmer. 2023. GSR+ Unit. https://shimmersensing.com/product/shimmer3-gsr-unit/
  34. StarVR. 2020. StarVR One. https://www.starvr.com/product/
  35. Take-over performance and safety analysis under different scenarios and secondary tasks in conditionally automated driving. IEEE Access 7 (2019), 136924–136933.
  36. Measurement and prediction of driver trust in automated vehicle technologies: An application of hand position transition probability matrix. Transportation research part C: emerging technologies 124 (2021), 102957.
  37. Driver emotion recognition for intelligent vehicles: A survey. ACM Computing Surveys (CSUR) 53, 3 (2020), 1–30.
  38. Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles. In Proceedings of the 2022 International Conference on Multimodal Interaction. 468–475.
  39. Detection of Perceived Discomfort in SAE L2 Automated Vehicles through Driver Takeovers and Physiological Spikes. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1717–1722.

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